An AI humanizer in Bangladesh gets used for two very different jobs, and most pages only admit to one. The advertised job is polishing an AI-assisted draft. The actual reason most people arrive is that a detector flagged something they wrote themselves, and Bangladeshi English gets over-flagged for reasons that have nothing to do with the writing.
So this page covers both, in that order of honesty. There is a free path that needs no card at all, which matters more here than in markets priced in dollars by default.
Two audiences, students and the freelance economy, and the same underlying mechanism hits both.
An average false positive rate of 61.3% across seven detectors on TOEFL essays by non-native writers, their measurement rather than ours between them (Liang et al., 2023); native-written controls in the same study were barely flagged.
The mechanism is uncomfortable but simple. Detectors measure how predictable your word choices are and how much that varies between sentences. Careful second-language English draws on a more deliberate vocabulary, keeps closer to reliable sentence patterns, and uses idiom sparingly, because getting idiom wrong is costly. All of that is good discipline. All of it lowers statistical surprise, which is what these systems read as machine-written.
Bangladesh has a second exposure the student case doesn't cover: a large freelance writing and content economy where a client-side detector flag can cost a contract with no appeal process at all. There the question isn't academic integrity, it is whether you can show a client how the draft was built. Version history is the answer there too.
Bangladesh has two versions of this problem and they need the same response.
If a university flagged it, the useful artifact is a record of the work accumulating: version history, dated drafts, your outline. A rewrite overwrites that record in the same action that changes the prose, which is why it's the wrong first move.
If a client flagged it, you are in a worse position procedurally, because there's usually no appeal process at all and no obligation on them to show you the evidence. What settles those conversations is the same thing: a document history the client can look at, plus the published research on why second-language English gets over-flagged. Send both. What to gather is here.
The free path is 3 rewrites a day at up to 260 words with no account; a free account gets 3 actions a day, scan or rewrite in any mix, at up to 260 words per rewrite. That is a real constraint, so spend it where the draft is actually weak rather than running whole documents through it.
Practically: find the two or three paragraphs that read flattest, rewrite those, and leave the rest. A detector score is a document-level number, so fixing the worst sections moves it more per word spent than a uniform pass does.
Then check the pair. Did a citation change what it claims, did a technical term get swapped, did a hedge disappear? Use Light for anything with a reference in it; Balanced suits general prose; Maximum replaces most of the wording and is a paid mode. The full check is here.
We can't tell you whether a rewrite clears a specific institution's checker. Nobody selling a humanizer can see another system's verdict, and those systems retrain against rewriting techniques, so a guarantee would be a claim about something we don't observe.
What we do is score before and after with our own detector, labelled as ours, and highlight which words changed. Useful, and not a prediction.
On language: the detector is built and measured for English. It reads Bangladeshi English as English. It doesn't score Bangla, and we won't imply it does.
Ours runs 3 rewrites a day at up to 260 words each with no account and no card, and a free account gives 3 actions a day, scan or rewrite in any mix, at up to 260 words per rewrite in Light and Balanced modes. That is enough for the paragraph-at-a-time workflow this page recommends. Much of this category asks for payment details before you see output on your own writing, which is the main thing that narrows the practical shortlist from Bangladesh.
Because they measure predictability rather than quality, and careful second-language English is less statistically surprising than a native-speaker average. A deliberate vocabulary, reliable sentence patterns and sparing idiom all reduce variation, and low variation reads as machine-written. Liang et al. (2023) measured an average false positive rate of 61.3% across seven detectors on TOEFL essays by non-native writers while barely flagging native-written controls.
Our detector is built and measured for English, so the before and after score is only meaningful on English text. The rewriter will accept Bangla but we publish no quality or accuracy claims for it and you should not rely on the score. If you draft in Bangla and translate into English, score the English version, since that is what will actually be read and checked.
Show how it was built rather than rewriting it. Version history, earlier drafts, your outline and notes are far stronger than any score, and a rewrite destroys exactly that evidence. It also helps to cite the research above, because client-side detectors inherit the same bias against second-language English, and a detector score is not a basis for terminating a contract on its own.
No. A rewrite replaces the sentences you would use to prove the work is yours and overwrites the draft history that supports you. Gather process evidence instead and bring the published research on detector bias. Use a rewriter on the next draft while you are writing it, not on a draft already under question.
Light in almost every case. It works on sentence rhythm and connective filler and leaves your vocabulary alone, which is the property second-language writing needs most. Balanced edits phrasing more broadly and suits general prose. Maximum replaces most of the wording and is a paid mode; the free tier covers Light and Balanced per our published plans.
No, and a guarantee is the clearest signal to walk away from a vendor. Nobody selling a humanizer can see what your institution’s checker outputs, and those systems retrain specifically against rewriting techniques. What a rewrite demonstrably does is change the statistical properties detectors read; whether that is enough in a specific case is not knowable from here.
Score a draft before anyone else does.
See the detector →Why detectors over-flag second-language English, with the research.
Read the evidence →What to gather if you wrote it and were flagged.
See the evidence list →The four things that drift first in a rewrite.
Read the method →The full tool, scored before and after by our own detector.
See the tool →What our own score cannot tell you.
See the limits →Score first, find the two paragraphs that actually read flat, rewrite those. 3 scans a day at up to 1,000 words, no account, no card.